1. Field of the Invention
This invention relates generally to communication systems, and, more particularly, to wireless communication systems.
2. Description of the Related Art
Conventional wireless communication systems provide wireless connectivity using a network of access nodes such as access points, base stations, base station routers, and the like. Each access node allocates resources to support wireless communication over an air interface with one or more access terminals such as mobile units, mobile phones, smart phones, network interface cards, notebook devices, laptops, desktops, and the like. The air interface resources are usually defined in terms of the available time slots, channel codes, frequencies, bandwidths, transmission powers, and the like. Access nodes or other network entities include schedulers that allocate resources to the access terminals for wireless communication.
Access terminals are typically allocated a portion of the available resources based on a priority. For example, access terminals may be assigned a scheduling priority based on a quality-of-service (QoS) level and/or channel conditions associated with communication over the air interface. The QoS levels can be associated with a guaranteed bit rate (GBR) that is promised to the associated access terminal. Alternatively, some users or services may opt for best effort allocation of resources, which does not guarantee a particular bit rate and instead attempts to fairly distribute the available resources among the best effort users. In some cases, maximum bit rates may also be imposed on user traffic over the air interface. Scheduling algorithms and/or admission control algorithms attempt to balance the competing demands of various QoS and/or best effort users.
One algorithm defines the priority for each user as the product of a weight determined by the QoS level of the user and a ratio of the current channel rate to a long-term average throughput or bit rate. The weight is an exponential function of a normalized difference between the user's guaranteed bit rate and the average bit rate over a time window. Consequently, the weight increases (or decreases) exponentially when the user's throughput falls below (or rises above) the guaranteed bit rate. The weight is also a function of the duration of the error between the guaranteed bit rate and the average bit rate or throughput so that the increase or decrease in the weight is further amplified if it persists over time. The net effect of this aggressive exponential weighting factor is to rapidly drive the average bit rate towards the guaranteed bit rate and concurrently drive the weight towards the best effort weight of W=1.
Although the exponential weighting scheme can effectively maintain the guaranteed bit rates, it comes at a cost. For example, the inventors of the present application have demonstrated that the aggressive exponential weighting scheme can force the scheduler to allocate non-optimal channels and consequently can degrade the overall efficiency of the system. For example, extra resources may be needed to meet the GBR requirements of QoS users in non-optimal conditions, which may reduce the resources available to BE users. Furthermore, scheduling algorithms struggle to maintain the guaranteed bit rates of users that are located near cell edges when the scheduler employs the exponential weighting technique. The net effect is that the overall throughput is reduced relative to the optimum allocation.